An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

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Not AvailableSafflower is a traditional oilseed crop in the world. Its seed oil is a healthy edible oil containing high amount of unsaturated fatty acids. Genetically diverse exotic cultivars are valuable germplasmfor introducing new diversity in safflower improvement programmes. In this study, we characterized safflower cultivars of India (30) andMexico (23) comprising varieties, hybrids and advanced lines developed over 50 years for genetic distinctiveness using 38 simple sequence repeat (SSR) loci. Genetic diversity estimates across cultivar groups (total, India and Mexico) were as follows: mean number of alleles (3.2, 3.1, 2.6), expected heterozygosity (0.42, 0.37, 0.37) and polymorphism information content (0.36, 0.33, 0.32) respectively, which suggested narrow SSR allelic diversity within and between cultivar groups. However, distance-based cluster analysis (neighbour-joining tree) and model-based STRUCTURE analysis revealed that safflower cultivars of India and Mexico, with the exception of a few, formtwo genetically distinct groups. High level of genetic variation explained between the populations (40%) and Fst estimate (0.4) suggested that the cultivar groups were highly differentiated with limited gene flow supporting a strong genetic structuring. High oil *38% and high oleic (73–79%) contents of a subset of Mexican safflower varieties and advanced lines were confirmed in field trials in India. These exotic sources from Mexico are valuable for safflower breeding programmes in India to develop new cultivars with high oil yielding potential and high oleic acid content,which is the current market demand.Not Availabl